Restricted Boltzmann Machines (RBMs) [1]} attracted considerable attention in the past years, mainly due to their simplicity, high-level parallelism, and comprehensive representation capacity. Such models stand for stochastic neural networks based on energy principles and guided by physical laws. Usually, these networks learn in an unsupervised fashion [2]} and are applied in various problems, e.g., image reconstruction, collaborative filtering, and feature extraction.
